The Financial Toy Room SIMulator

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Financial Toy Room (FTR)

The Financial "Toy-Room" (FTR) is a micro-founded
simulation environment for both decentralized and centralized trade in
a financial asset. Some aspects of the representation are
intentionally kept very simple, and in a sense abstract: quite diverse
models may indeed be implemented as particular instantiations of the
general template presented in the following. The general motivations
for FTR are to a good extent akin to those inspiring already existing
computer-simulated "artificial markets" of a financial asset,
such as those by Marengo and Tordjman (1996), Rieck (1994), Beltratti
and Margarita (1992), and Arthur et al. (1997)

Obvious common points of departure are (i) the acknowledgment
of the limitations of models of market dynamics centered upon the
behavior of a mythical representative agent endowed with unbiased
forward-looking expectations, and conversely (ii) the challenge
of nesting the theory into an explicit account of heterogeneous,
interacting agents. Some forms of heterogeneity in information and
beliefs can be incorporated into analytically tractable models (see
for example the information-related heterogeneity in Grossman and
Stiglitz, 1976 and 1980, the diversity of beliefs associated to the
presence of noise traders in De Long et al. 1990, 1991 and
Schleifer and Summers, 1990, see also Blume and Easley, 1990).
However, analytical tractability poses heavy constraints on the forms
and degrees of heterogeneity, as well as the forms of learning, one
can handle.

Moreover, one is forced to analyze almost exclusively limit
(equilibrium) properties of the models, and neglect finite-time
properties which might nonetheless be the most relevant for comparison
with empirical data. The artificial market approach tries to
overcome these drawbacks by explicitly simulating populations of
interacting agents who might endogenously evolve beliefs, behaviors
and mental models (Marengo and Tordjman, 1996): FTR has been
build on the grounds of the same basic philosophy.

In FTR we stress two type of strategic investment approach to
financial markets that clearly characterize two kind of different
agents: fundamentalists and chartists. Obviously each
of the two kind of agents can be modeled in different ways: from very
simple algorithms to very complex ones.

FTR allows also to mix the strategy types in the "heads" of single
agents, although, of course, the more complex are the mental
models of the agents the greater the amount of code that the
analyst has to write.

Furthermore we emphasize the specific architectural traits of
markets, i.e. the mechanisms on the basis of which interactions
among agents occur. We have characterized, as initial instances,
two different models; namely, first, a centralized market -- that
is, in actual fact, an auction, and, second, a quite general
instance of a decentralized market that is the more general model
for decentralized trading that you can design. In decentralized
markets an agent who seeks to trade can meet a certain number of
traders (notionally ranging from one in a pairwise matching setup
to every agents) that accept (i.e. are available) to trade.
Seeking/accepting attitudes allow to model a wide range of market
structures. For instance dealers or specialists, present in
certain type of markets, can be modeled as ``ever-accepting''
agents.

FTR, by allowing different physics of interaction and different
types of behaviours which are legal in a given market
architecture, makes it possible to represent a wide ensemble of market
architectures.

FTR, when compared to other artificial markets, enlarges the
scope of analysis in several respects.

First, FTR entails easy experimentation with different types of
agents, both in terms of behavioral and cognitive patterns, and in
terms of learning procedures.

Second, it allows exploration of the properties of different
architectural and institutional traits, especially with respect to the
physics of interactions (e.g. the specific mechanism for
decentralized encounters), and the information availability by
individual traders or groups of them.

Third FTR allows to represent time in alternative and possibly quite
sophisticated ways. In an artificial world, modeling time is indeed a
tricky issue. Basically, there are two alternative ways to run clocks
in an artificial market. First, one may think of a time-driven
model in which one attempts to reproduce some exogenous
clock whereby for each unit of time a variable number of events
may occur. Second, one may build event-driven models, whereby
the clock counter is increased by one unit whenever some event
occurs. Both representations are possible in FTR. In fact, FTR
embodies an explicit time-embedding of events that allows us to easily
represent asynchronous and/or diversely paced ``clocks'' for diverse
classes of events at the system and individual level (e.g. buying and
selling -- trading, vs. accessing news, vs. making trading
decisions, vs. learning, vs. updating). Relatedly, FTR naturally
allows us to study the dynamic properties of the system on different
time-scales. And moreover it allows to experiment with both models of
time: e.g. time-driven model for the market clock and event-driven for
the clocks in the minds of agents.

As such, we see FTR as the artificial counterpart of
micro-structural studies (cf. Frankel, Galli and Giovannini, 1997 and
Goodhart and Payne, 1996). There is a long and growing list of
stylized facts to a good extent still in search of an
interpretation (for complementary discussions, see Brock, 1997,
Frankel, Galli and Giovannini, 1997, Goodhart and Figliuoli, 1991,
Guillaume et al. 1997). With FTR, one can investigate under what types
of cognitive/behavioral patterns and learning processes, and what
types of interaction and information regimes, one is able to reproduce
the regularities detected in empirical markets as emergent properties
of the artificial market dynamics. Relatedly, with FTR it is easy to
undertake thought experiments on the effect of varying
microfoundations and varying institutional set-ups upon system
dynamics. Two broad questions come immediately to mind, namely:

Holding the institutional set-up (i.e. interaction and information
regimes) constant, what happens as one varies the "ecology" of
cognitive/behavioral patterns and learning processes?

So, for example, simulation experiments will allow us to assess
whether observed statistical regularities (e.g. the so called "ARCH"
effects, "fat tails", etc.) are generic properties, holding over a
wide range of interaction regimes and "ecologies", or conversely,
whether such regularities are conditional to very specific
institutional set-ups and distributions of agents' "types".

Let us emphasize that FTR is still at a construction stage, still
operating under modeling simplifications which shall be eventually
overcome. So, for example, for the time being, each agent can exchange
only one unit of asset in each transaction it is engaged in. In the
near future we will modify the code to allow rules by which agents can
exchange more than one unit and, further down the line manage their
portfolios.